Eating‐disorder symptoms and syndromes in a sample of urban‐dwelling Canadian women: Contributions toward a population health perspective
Bibliographic record
Abstract
OBJECTIVE: We estimated the prevalence of eating disorders and maladaptive eating behaviors in a population-based sample and examined the association of maladaptive eating with self-rated physical and mental health. METHOD: A sample of 1,501 women (mean age = 31.2 years, SD = 6.2) were recruited using random-digit dialing to participate in a 20-min telephone interview about eating behaviors. RESULTS: Weighted frequency analysis showed the prevalence of frequent binge-eating to be 4.1%, that of regular purging to be 1.1%, and that of frequent compensation to be 8.7%. Although we found none of the women to meet full criteria for anorexia nervosa, 0.6% met criteria for bulimia nervosa, 3.8% provisional criteria for binge eating disorder, and 0.6% criteria for a newly proposed entity, purging disorder. As many as 14.9% fell into a residual category representing subthreshold, but potentially problematic variants of eating disturbances. Logistic regression analyses showed that clinical-level maladaptive eating attitudes and behaviors predicted self-rated physical- and mental-health problems after sociodemographic factors were controlled. DISCUSSION: This population-based survey provides prevalence estimates of BN, BED, and purging disorder that are compatible with those of recent epidemiological studies and shows that maladaptive eating attitudes and behaviors represent a substantial population burden.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".